Sentiment mapping in a media content item

ABSTRACT

A media content item is evaluated for its “sentiment states.” That is, segments of the content item are determined to be, for example, “happy,” “exciting,” “sad,” “funny,” and the like. A “sentiment map” is created that delimits segments of the content item and contains the sentiment-state keywords associated with the segments. Some maps include an amplitude for each assigned sentiment keyword and a confidence value for the segment delimitation and for each keyword. As an exemplary use of the sentiment map, an advertisement broker matches the sentiments of his advertisement offerings with segments of a content item in order to place appropriate advertisements at times when they would be most favorably received. In another example, a recommender system recommends to a user a content item whose sentiment map compares favorably to that of a content item already enjoyed by the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to U.S. patent application Ser. Nos.13/412,576 and 13/419,597, filed on an even date herewith.

FIELD OF THE INVENTION

The present invention is related generally to electronic media and, moreparticularly, to evaluating sentiments in electronic media.

BACKGROUND OF THE INVENTION

Recently, advertisers have begun to look beyond “traditional” media(e.g., magazines and television) and have begun looking to “new media”(e.g., online and mobile services) to increase the effectiveness oftheir advertising campaigns. Online advertising is appealing because anadvertiser can put an advertisement in front of an audience that isactively searching for information. This allows the advertiser to tapinto the needs of people prepared to buy rather than, as in thetraditional approach, blindly sending advertisements to people who aresimply watching television or reading a print medium.

However, even as people spend more time online, traditional media remainvery important (and they still receive the majority of advertisingdollars). Indeed, rather than simply replacing traditional media timewith online time, many people are beginning to combine traditional andnew media. For example, while they watch television, they also payattention to a “companion device” (e.g., a laptop computer or a smartphone). These users then receive programming (which can includeadvertising) both through the television and through the companiondevice.

These media are very different and the programming (which, again, caninclude advertising) delivered through them can be very different. Thiscan cause conflicts when, for example, a user, while watching a veryromantic movie, receives a funny advertisement on his companion device.The advertisement may, in itself, be unobjectionable to the user, butthe conflicting sentiments between the movie and the advertisementconfuses the user and may lead to “advertising dissonance.” Thedissonance reduces both the user's enjoyment of the movie and theadvertisement's effectiveness for this user.

BRIEF SUMMARY

The above considerations, and others, are addressed by the presentinvention, which can be understood by referring to the specification,drawings, and claims. According to aspects of the present invention, amedia content item (e.g., a movie, television program, or audio segment)is evaluated for its “sentiment states.” That is, segments of thecontent item are determined to be, for example, “happy,” “exciting,”“sad,” “funny,” and the like. To assign sentiments, any informationabout the media clip may be evaluated such as the video and audio of theclip, metadata concerning the clip (e.g., close-captioning informationand a description in an electronic program guide), and evensocial-networking responses to the content item. A “sentiment map” iscreated that delimits segments of the content item and contains thesentiment-state keywords associated with the segments.

In some embodiments, the delimitation of segments is based on thesentiment evaluation itself. Some embodiments also allow the delimitingand evaluating to be performed in real time (that is, while the mediacontent item is being received), which can be important when socialresponses are included in the evaluation.

In addition to the delimitations and sentiment keywords, someembodiments include in the sentiment map an amplitude for each assignedsentiment keyword (e.g., how funny was it?) and a confidence value forthe segment delimitation and for each keyword.

In some situations, multiple keywords are assigned to the same segment.Segments may overlap, and a segment may encompass the entire mediacontent item. (This can be useful when the content item is very short.)

The evaluation, in some embodiments, considers information (e.g.,profile and demographics) about a user actually watching the mediacontent item. In this situation, an attempt is made to create asentiment map personalized to this user's preferences. For example, apersonalized sentiment map may tag a segment of a content item asexciting because this particular user is known to be an enthusiast fordog shows. The same segment is not tagged as exciting in anon-personalized sentiment map if the general public does not share thisenthusiasm.

The sentiment map thus created can be used in a number of applications.As a first example, the map is considered by an advertisement broker.The broker uses the map to match his advertisement offerings withsegments of the media content item and can thus place appropriateadvertisements at times when they would be most favorably received. Theadvertising campaign may be directed to a primary device (on which auser is viewing the media content item) or to a companion deviceassociated with the same user. In some embodiments, advertisementbrokers can submit bids to have their advertising placed duringadvantageous times. A particularly sophisticated system couldre-evaluate the content item after a bid was accepted, the re-evaluationbased on additional information that just became available (e.g., socialresponses to a live broadcast). If the re-evaluation shows that theoriginal evaluation, on which the bid was based, was not very accurate,given the additional information, a refund of a portion of the bid couldbe provided to the advertisement broker.

As a second example of an application that uses the sentiment map, arecommender system can consider the sentiment map of a media contentitem that was enjoyed by a user. By comparing this sentiment map withsentiment maps of other content items, the recommender can choose acontent item whose sentiment map is similar to the sentiment map of thecontent item enjoyed by the user. (When comparing, the recommender canalso consult a preference profile for the user). The recommender thenrecommends this other content item to the user.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

While the appended claims set forth the features of the presentinvention with particularity, the invention, together with its objectsand advantages, may be best understood from the following detaileddescription taken in conjunction with the accompanying drawings ofwhich:

FIG. 1 is an overview of an exemplary environment in which the presentinvention may be practiced;

FIG. 2 is a generalized schematic of some of the devices shown in FIG.1;

FIG. 3 is a flowchart of an exemplary method for creating a sentimentmap for a media content item;

FIG. 4 is a flowchart of an exemplary method for using a sentiment mapto place advertisements; and

FIG. 5 is a flowchart of an exemplary method for using a sentiment mapto recommend a media content item.

DETAILED DESCRIPTION

Turning to the drawings, wherein like reference numerals refer to likeelements, the invention is illustrated as being implemented in asuitable environment. The following description is based on embodimentsof the invention and should not be taken as limiting the invention withregard to alternative embodiments that are not explicitly describedherein.

Aspects of the present invention may be practiced in the representativecommunications environment 100 of FIG. 1. Connected together via any orall of various known networking technologies 102 are media contentproviders (e.g., cable television head-end servers and the like) andother servers such as media analyzers 104, advertisement brokers 106,and recommender systems 108. (The functions of these servers arediscussed below.) For ease of illustration, only one of each type ofserver 104, 106, 108 is shown, but multiples of each can exist and canwork together, as discussed below.

The servers 104, 106, 108 provide, via the networking technologies 102,sentiment analysis of media content and related services to end-userdevices. Traditional end-user devices are supported by “wireline”network technologies (e.g., fiber, wire, and cable) 112. For example, aset-top box 114 generally receives television programming from variousmedia content providers and provides a user interface (e.g., aninteractive program guide) for selecting and viewing content from thecable provider. A digital video recorder (not shown) can storeprogramming for later viewing. Video content may be viewed on atelevision monitor 116. In some situations, a laptop computer 118 canaccess both television content and web-based services either wirelesslyor via the wireline network 112. A home gateway, kiosk, digital sign, ormedia-restreaming device (not shown) are other possible end-userdevices.

(A media-restreaming device transfers content between disparate types ofnetworks. For example, it receives content from a cable televisionsystem 112 and then transmits that content over a local radio link suchas WiFi to the cellular telephone 110. The media-restreaming deviceusually operates in both directions to carry messages between thenetworks. In some embodiments, aspects of the present invention arepracticed by a media-restreaming device.)

Television programming can also be delivered to non-traditionalsubscriber devices such as the cellular telephone 110. This telephone110 communicates wirelessly to a wireless base station (not shown butknown in the art) to access the public switched telephone network, theInternet, or other networks to access web-based services as well as thetelevision-delivery services provided by the media content providers.

Wireless and wireline network technologies generally support two-waytraffic: Media content and related information are delivered to theend-user devices 110, 114, 116, 118, and requests go “up” to the servers104, 106, 108.

A typical user may split his attention by interacting with any or all ofthe end-user devices 110, 114, 116, 118 at roughly the same time or in atemporally overlapping manner. Examples in the present discussionusually assume that the user is watching the television monitor 116 andpossibly interacting with it through the set-top box 114. In somesituations, the user at least occasionally gives some of his attentionto a “companion device” such as the cellular telephone 110.

To illustrate aspects of the present invention, consider a user watchinga television program on the television monitor 116. A media analysisapplication analyzes the television program (possibly before the programis delivered to the user or possibly in real time) for sentiments. Themedia analysis application produces a sentiment map of the televisionprogram. The map lists segments of the program along with sentiments(e.g., “happy,” “exciting,” unknown) associated with the segments. (Forthe sake of simplicity, the present discussion assumes that the mediaanalysis application is fully embodied on one device, but in otherembodiments this application can reside at least partially within thehead-end of a cable provider, on a web server 104, on an end-user devicesuch as the cellular telephone 110 or set-top box 114, or on somecombination of these.)

In some embodiments, the sentiment map is then made available toservices such as an advertisement broker 106. By reviewing the sentimentmap, the advertisement broker 106, determines, for example, that thenext 30 seconds of the television program are “exciting.” Theadvertisement broker 106 finds an advertisement whose sponsor wishes theadvertisement to be associated with “exciting” content. Theadvertisement broker 106 then places a bid to place that advertisementeither on the television monitor 116 or on the user's companion device110. If the bid is accepted, then the advertisement is placed temporallynear the exciting segment of the television program, to the satisfactionof the advertisement sponsor. (In some situations, the advertisement isdelivered to the set-top box 114, and the set-top box 114 delivers theadvertisement to the television monitor 116. These sorts of connectionoptions are well known in the art and need not be further discussed.)

Particular aspects of the media analysis application are discussed belowin conjunction with FIG. 3. Two examples of applications that use thesentiment map are discussed below in conjunction with FIGS. 4 and 5.

FIG. 2 shows the major components of a representative server 104, 106,108 or end-user device 110, 114, 118. Network interfaces (also calledtransceivers) 200 send and receive media presentations and messages suchas the sentiment map. A processor 202 controls the operations of thedevice and, in particular, supports aspects of the present invention asillustrated in FIGS. 3 through 5, discussed below. The user interface204 supports a user's (or administrator's) interactions with the device.Specific uses of these components by specific devices are discussed asappropriate below.

FIG. 3 presents a method for creating a sentiment map. The method beginsin step 300 when the media analyzer application receives a media contentitem. First note that “media content item” is meant very broadly: It canbe a television program or movie, but it could also be a sound-onlyclip, a message of any kind (e.g., an e-mail with attached or embeddedcontent), a telephone call with or without accompanying video, or evenan advertisement or an interactive computer game. For simplicity's sake,the following discussion often assumes that the media content item is atelevision program presented on the television monitor 116, but allthese other possibilities should be kept in mind.

“Receiving” encompasses many possibilities. If the media analyzer isembodied on a network server 104, then it can download the full contentitem and process it according to the remaining steps in FIG. 3. This“offline” method has several advantages. First, the media analyzer 104can take whatever time it needs to perform the analysis. Second, becausethe media analyzer 104 can review the entire content item, it can betterestimate the beginning and end of a particular segment. (See thedetailed description of delimitation that accompanies step 304 below.)Third, a network server 104 can perform the sentiment analysis once fora given content item and then provide the map as needed, rather thanhaving each recipient of the content item perform its own analysis. Mostof these advantages also apply if the analysis is done offline by alocal user device, e.g., by the user's laptop computer 118 analyzing acontent item stored on the user's digital video recorder.

There are cases, however, where offline processing is simply not anoption. This is especially true when the media analyzer cannot acquireaccess to the full media content item significantly before the sentimentmap is required. The content item may be a live event streamed to auser's television monitor 116. Even if the content item is not actuallya live broadcast, if it is being shown for the first time (e.g., thenewest episode of a television series), then it is unlikely that themedia analyzer will be allowed access to all of the content item beforeit is sent to users. In these situations, the media analyzer attempts tocreate the sentiment map in “real time,” that is, while the content itemis being received. In terms of FIG. 3, this means that steps 300 through308 (and possibly even steps 310 and 312) are performed, in some sense,concurrently rather than consecutively. In one embodiment, the mediaanalyzer application runs on the user's set-top box 114, and it analyzesthe programming as it is being received from the cable system 112 andthen sent to the television monitor 116. In some embodiments, theprogramming can be buffered and delayed for a few seconds by the set-topbox 114 to allow the media analysis to “keep ahead” of the point in thecontent item currently being viewed.

The next two steps, 302 and 304, are, in most embodiments, inseparable.The present discussion attempts, as far as possible, to present thesetwo steps independently, but it should be kept in mind that the detaileddiscussion of each step informs that of the other.

In step 302, the media content item is analyzed for sentiments. Ideally,every point in time of the content item is associated with one or moresentiment-state keywords that express the mood of the content item atthat point in time. (See also the discussion of step 306 below.) Step304 is a recognition that, generally, the “point in time” is actually arelatively short temporal segment of the content item. For example, thesegment from 20 seconds into the content item to 30 seconds isconsidered to be “happy,” and the segment from 53 seconds to 1 minute,20 seconds is “exciting.” Clearly, the sentiments associated with asegment and the delimitation of the segment are interrelated. In fact,it is usually the sentiment analysis itself that determines thedelimitation of the segments. That is, when the sentiment “happy” isfound at one time, the surrounding time in the content item can beanalyzed to determine the approximate temporal extent of this happiness.That temporal extent then defines the limits of the segment that isassociated with happiness. Sometimes, metadata are available with thecontent item that help in delimiting the segments (e.g., a scene listwith start and stop times).

Focusing again on step 302, many inputs may be used by the mediaanalyzer application. Soundtracks generally provide distinct cues toviewers to know what sentiment is expected (e.g., a low, slow cadence ina minor key is usually associated with sorrow or loss by human hearers),and well known tools are available to the media analyzer to extract thisinformation. The words being said, and how they are said, also oftencontain clear sentiment cues.

Similarly, the video itself may contain cues, such as the amount of timebetween cuts (exciting scenes usually cut very often), light levels, theamount of shadowing of a speaker's face, and how the main characters areplaced with respect to one another.

In some situations, the media analysis application can use metadataassociated with the media content item such as close-captioninginformation, an electronic program guide listing, or a script withstaging instructions.

A sophisticated media analysis application can mine further sources ofinformation. One intriguing possibility considers “social-networking”metadata, that is, online posts and commentary produced by viewers ofthe media content item. These comments are often produced while thecontent item is being viewed. Other comments are posted later. All ofthese comments can be reviewed in an attempt to refine the sentimentanalysis. Consider a case where the analysis application reviews thevideo and audio of a segment and, based on that review, associates an“exciting” keyword with that segment. However, a scan of online postsreveals a surprising number of viewers who found this same segment to bepoorly conceived and woodenly acted. These viewers were disappointed andbored with the segment. The analysis application can take these reviewsinto account by downgrading a confidence value (see the discussion ofstep 306 below) associated with the “exciting” keyword or even byassigning both an “exciting” keyword and a “boring” keyword to the samesegment, the former indicating the director's intent, and the latterindicating the result actually achieved.

Some embodiments use further sources of information during theevaluation. The media analysis application can attempt to map aparticular user's expected response to segments of the media contentitem. (This is very specific as compared to the above discussion ofonline posts, where the posts reflect the responses of the generalpopulation to a segment.) Here, the information specific to a particularuser can include, for example, a preference profile of the user,purchasing and other behavioral information, demographic information,and even current situational information such as the presence of friendsand family during the viewing of the content item. All of thisinformation can be considered when making a sentiment map personalizedto this user. In the example mentioned in the Summary, the personalizedsentiment map may tag a segment as “exciting” because this particularuser is known to be an enthusiast for dog shows. The same segment maynot be not tagged as exciting in a non-personalized sentiment map if thegeneral public does not share this enthusiasm.

Moving now to step 304, in some situations the entire media content itemis made up of only one segment. If, for example, the content item isvery short, such as a 30-second advertisement, then it may present onlyone sentiment state throughout. Generally, however, several segments canbe defined within one content item. The segments may even overlap as,for example, when, partway through a “happy” segment, an “exciting”segment begins.

The delimitation produced by the media analysis application is oftenimprecise. To address this possibility, sophisticated embodiments attachto each delimitation a confidence value. For example: “It is 90%probable that this happy segment lasts for at least the next 20 seconds,and 65% probable that it continues for a further 15 seconds after that.”

It should be noted that, in many situations, the delimited segments donot encompass the entire content item. Some portions of the content itemmay simply not express a sentiment, or the media analysis applicationcannot discover the particular sentiment intended.

In step 306, one or more sentiment-state keywords are associated withdelimited segments. Any types of keywords can be used here including,for example, “happy,” “sad,” “exciting,” “boring,” “funny,” “romantic,”“violent,” “successful,” and the like. (As noted just above, the mediaanalysis application may not be able to associate any sentiment-statekeyword with a particular segment. In some embodiments, the keyword“unknown” is then used.) As the tools available to the media analysisapplication improve, it is expected that the list of keywords willincrease.

Some applications that use the sentiment map (see in particular thediscussion accompanying FIGS. 4 and 5 below) are interested in knowingnot only the sentiments associated with a segment, but the amplitudes ofthose sentiments, that is, not only “this segment is funny,” but also“this segment is very, very funny” or “this is the funniest segment inthe entire media content item.” The sentiment map can include confidencevalues for each associated sentiment keyword and the amplitude (if any)of the keywords.

Step 308 stores the information generated by the evaluation anddelimitation in a sentiment map. Any number of types of datarepresentation are possible here. It should be noted that overall, themap is very much smaller than the media content item itself. The map canbe sent to applications that need it (step 310). (See the discussion ofexample applications accompanying FIGS. 4 and 5 below.) If the map isneeded in real time, then parts of it can be sent out to waitingapplications as they become available (e.g., segment by segment).

In optional step 312, the sentiment mapping of a segment is revisedbased on further information, presumably information that was notavailable when the original sentiment map was created for the segment.The online social-network posts, mentioned above in relation to step302, are especially relevant here. Because some applications may need touse the sentiment map in real time (FIG. 4 gives an example), the mediaanalyzer produces an initial sentiment map as quickly as it can withwhatever information is at hand. Some online comments may be posted toolate to be considered for this real-time analysis, but they can be usedin the re-evaluation of step 312.

Portions of the media content item itself may also count as “furtherinformation” for the purposes of step 312. As mentioned above in thediscussion of offline vs. real-time processing in relation to step 300,offline processing has the advantage that it can view the entire mediacontent item when deciding how to delimit each segment. A real-timemedia analyzer does not have that option, but, after receiving more oreven all of the content item, it can achieve much of the results ofoffline processing by re-considering the delimitation of segments as aresult of the re-evaluation of step 312.

Many types of applications can make use of the sentiment map. Forexample, the producer or provider of the media content item can reviewthe map (especially when online posts are considered when making themap) to compare the director's intent with the actual effect achieved bythe content item. FIGS. 4 and 5 present two other possibilities.

FIG. 4 presents a method whereby an advertisement broker 106 uses thesentiment map when deciding where to place advertisements. The methodbegins in step 400 when the advertisement broker 106 receives thesentiment map. As discussed above, this may be a map of the entire mediacontent item, produced offline by, say, a media analysis server 104.Also of interest is the case where the advertisement broker 106 receivesthe map segment by segment as produced in real time, as, for example,the content item is being distributed to viewers via the cabletelevision system 112.

In any case, the advertisement broker 106 in step 402 compares thesentiment keywords and delimitation of a segment with a candidateadvertisement. For example, the sponsor of this particular advertisementmay have told the advertisement broker 106 that this advertisementshould be shown in conjunction with “exciting” segments of the mediacontent item.

In another case, the sponsor did not so inform the advertisement broker106 of its intent. Instead, a sentiment map is created for theadvertisement itself (as noted above in relation to step 304, this mapmay only contain a single segment). That is, it is recognized that anadvertisement is also a media content item in its own right and can besubjected to the same type of analysis described above in reference toFIG. 3. In this case, the analysis may show that the advertisement isboth “exciting” and “upbeat.” The advertisement broker 106 can theninfer that this advertisement would be best received if it were shownduring “exciting” or “upbeat” segments of the media content item thatthe user is watching on his television monitor 116.

Regardless of how the advertisement broker 106 determines whichsentiments are most favorable to this advertisement, the advertisementis compared against the sentiment map. The comparison may also considerthe amplitude of the sentiment and the delimitation of the segment(i.e., the segment may be too short to use with this advertisement).Confidence values in the map can also be considered: If the content itemis drawing to a close, and if the advertisement broker 106 is taskedwith presenting a given number of advertisements during the presentationof the content item, then the advertisement broker 106 may have tosettle for a less than ideal, or for a less confidently ideal, segmentfor the advertisement. If a match is favorable enough (usually asdefined by the advertisement sponsor or as inferred by the advertisementbroker 106), then the advertisement broker 106 proceeds to step 404 awhere it attempts to place this advertisement in conjunction with thissegment

Several implementations are possible for “attempting to place” theadvertisement. In the example of step 404 b, the advertisement broker106 submits a bid to place the advertisement at a given time. The bidcan include a proposed fee to be paid to the content provider, and canspecify whether the advertisement is to be placed within the stream ofthe media content item itself (as viewed on the television monitor 116to continue the pervasive example of this discussion) or on a companiondevice, such as the user's cellular telephone 110. Of course, if thelatter is desired, then well known methods can be applied to determinewhether or not the user has a companion device, whether or not thatdevice is turned on, and whether or not the user is interacting withthat device (indicating that the companion device has captured at leastsome of the user's attention). If the bid is accepted, then theadvertisement is placed accordingly.

In step 312 of FIG. 3, the sentiment map is redrawn as more informationbecomes available to the media analysis application. In optional step406 of FIG. 4, the advertisement broker 106 reviews the revised map. Ifa bid was placed and accepted, but the revised map shows that thesegment was not all it was thought to be (e.g., based on viewer's onlineresponses, a supposedly “romantic” scene fell flat), then theadvertisement broker 106 can request a refund of part of the bid price.The possibility of a refund could make advertisement brokers 106 morewilling to place a reasonable amount of reliance on sentiment mappingand on placing bids based on these maps.

FIG. 5 presents a recommender system 108 that uses sentiment maps. Insteps 500 and 502, the recommender 108 receives sentiment maps of twomedia content items. The maps are compared in step 504, and, if thecomparison is favorable in some sense, then the two content items areassociated with one another in step 506. (This is a generalization ofthe special case discussed above in relation to step 402 of FIG. 4 wherea sentiment map of an advertisement is compared against the map of acontent item to see where the advertisement should be placed.)Generally, this means that the two content items have similar sentimentmaps. For example, two classic “tear jerkers,” though very different inplot and staging, may exhibit a similar progression of sentiments. (Thisis one reason, in fact, that such movies are called “formulaic:” Theformula generally refers to the sentiments evoked scene by scene inaddition to, or instead of, the plot elements.) Thus sentiment maps canbe used to categorize content items in a more meaningful fashion thancategorization by actors, setting, or plot elements. If a user is knownto like one content item, then a second content item with a similarsentiment map can be recommended to him in step 508. The user's reactionto the recommendation may help to improve the quality of the sentimentmapping.

In view of the many possible embodiments to which the principles of thepresent invention may be applied, it should be recognized that theembodiments described herein with respect to the drawing figures aremeant to be illustrative only and should not be taken as limiting thescope of the invention. For example, many other sentiment keywords arepossible, and other applications of sentiment mapping can be considered.Therefore, the invention as described herein contemplates all suchembodiments as may come within the scope of the following claims andequivalents thereof.

We claim:
 1. A method for associating a second media content item with afirst media content item, wherein the first media content item comprisesa first audio/video content and the second media content item comprisesa second audio/video content, the method comprising: receiving, by arecommender system, a first map of a first delimited segment of thefirst media content item, the first map comprising a firstsentiment-state keyword associated with the first segment and a firsttemporal delimitation of the first segment; receiving, by therecommender system, a second map of a second delimited segment of thesecond media content item, the second map comprising a secondsentiment-state keyword associated with the second segment and a secondtemporal delimitation of the second segment; comparing, by therecommender system, the first and second maps; and if a result of thecomparison is a favorable result for a temporal extent associated withthe first and second temporal delimitations, then associating, by therecommender system, the second media content item with the first mediacontent item.
 2. The method of claim 1 wherein the first and the secondmedia content items are selected from the group consisting of: a movie,a television program, a radio program, a segment of video, a segment ofaudio, a song, a music video, a personal message comprising audio/videocontent, a public-service message comprising audio/video content, aservice alert comprising audio/video content, a health-servicerecommendation comprising audio/video content, a web page comprisingaudio/video content, an e-mail message comprising audio/video content,and a telephone call comprising audio/video content.
 3. The method ofclaim 1 wherein the recommender system is selected from the groupconsisting of: a set-top box, a personal communications device, a mobiletelephone, a personal digital assistant, a personal computer, a tabletcomputer, a gaming console, a head-end server, a server, and a pluralityof servers.
 4. The method of claim 1 wherein the first delimited segmentcomprises the entire first media content item.
 5. The method of claim 1wherein the first map further comprises a confidence value associatedwith the first temporal delimitation of the first segment.
 6. The methodof claim 1: wherein receiving the first map comprises receiving maps ofa plurality of segments of the first media content item; and wherein atleast two of the plurality of delimited segments temporally overlap. 7.The method of claim 1 wherein the first sentiment-state keyword isselected from the group consisting of: happy, sad, funny, successful,romantic, exciting, violent, unknown.
 8. The method of claim 1 whereinthe first map comprises an amplitude value associated with the firstsentiment-state keyword.
 9. The method of claim 1 wherein the first mapcomprises a keyword confidence value associated with the firstsentiment-state keyword.
 10. The method of claim 1 wherein the first mapcomprises a plurality of sentiment-state keywords associated with thefirst delimited segment.
 11. The method of claim 1 further comprising:sending, by the recommender system to a consumer of the first mediacontent item, a recommendation of the second media content item.
 12. Themethod of claim 1, wherein the second media content item is anadvertisement, and the first media content item is not an advertisement.13. A recommender system for associating a second media content itemwith a first media content item, wherein the first media content itemcomprises a first audio/video content and the second media content itemcomprises a second audio/video content, the recommender systemcomprising: a network interface configured for receiving a map of afirst delimited segment of the first media content item, the mapcomprising a first sentiment-state keyword associated with the firstsegment and a first temporal delimitation of the first segment, and forreceiving a map of a second delimited segment of the second mediacontent item, the map comprising a second sentiment-state keywordassociated with the second segment and a second temporal delimitation ofthe second segment; and a processor operatively connected to the networkinterface, the processor configured for: comparing the maps of the firstand second delimited segments; and if a result of the comparison is afavorable result for a temporal extent associated with the first andsecond temporal delimitations, then associating the second media contentitem with the first media content item.
 14. The system of claim 13,wherein the first and the second media content items are selected fromthe group consisting of: a movie, a television program, a radio program,a segment of video, a segment of audio, a song, a music video, apersonal message comprising audio/video content, a public-servicemessage comprising audio/video content, a service alert comprisingaudio/video content, a health-service recommendation comprisingaudio/video content, a web page comprising audio/video content, ane-mail message comprising audio/video content, and a telephone callcomprising audio/video content.
 15. The system of claim 13, wherein thesecond media content item is an advertisement, and the first mediacontent item is not an advertisement.